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    "\n",
    "## GeostatsPy: Trend Model Overfit Demonstration for Subsurface Data Analytics in Python \n",
    "\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
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    "### PGE 383 Exercise: Overfit Demonstration for Spatial Trend Modeling\n",
    "\n",
    "Here's a simple workflow to demonstrate the concept of overfit in spatial trend modeling.  Let's first review trend modeling.\n",
    "\n",
    "#### Trend in Subsurface Modeling\n",
    "\n",
    "Geostatistical spatial estimation methods assume stationarity of the input statistics. This may be represented as follows for the mean, cumulative distribution function and variogram of spatial variable, $x$ as:\n",
    "\n",
    "\\begin{equation}\n",
    "\\overline{x}(\\bf{u}) = \\overline{x} \\quad F_x(x:\\bf{u}) = F_x(x) \\quad \\gamma_x(\\bf{h};\\bf{u}) = \\gamma_x(\\bf{h}) \n",
    "\\end{equation}\n",
    "\n",
    "Yet, the subsurface is often not stationary. A common workflow is to model the nonstationary trend in the input statistic over the area of interest, $\\bf{u} \\in AOI$.  This is known as trend modeling.\n",
    "\n",
    "#### Trend Modeling\n",
    "\n",
    "Trend modeling is the modeling of local features, based on data and interpretation, that are deemed certain (known).  The trend is substracted from the data, leaving a residual that is modeled stochastically with uncertainty (treated as unknown).\n",
    "\n",
    "* geostatistical spatial estimation methods will make an assumption concerning stationarity\n",
    "    * in the presence of significant nonstationarity we can not rely on spatial estimates based on data + spatial continuity model\n",
    "* if we observe a trend, we should model the trend.\n",
    "    * then model the residuals stochastically\n",
    "\n",
    "Steps: \n",
    "\n",
    "1. model trend consistent with data and intepretation at all locations within the area of itnerest, integrate all available information and expertise.\n",
    "\n",
    "\\begin{equation}\n",
    "m(\\bf{u}_\\beta), \\, \\forall \\, \\beta \\in \\, AOI\n",
    "\\end{equation}\n",
    "\n",
    "2. substract trend from data at the $n$ data locations to formulate a residual at the data locations.\n",
    "\n",
    "\\begin{equation}\n",
    "y(\\bf{u}_{\\alpha}) = z(\\bf{u}_{\\alpha}) - m(\\bf{u}_{\\alpha}), \\, \\forall \\, \\alpha = 1, \\ldots, n\n",
    "\\end{equation}\n",
    "\n",
    "3. characterize the statistical behavoir of the residual $y(\\bf{u}_{\\alpha})$ integrating any information sources and interpretations.  For example the global cumulative distribution function and a measure of spatial continuity shown here.\n",
    "\n",
    "\\begin{equation}\n",
    "F_y(y) \\quad \\gamma_y(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "4. model the residual at all locations with $L$ multiple realizations.\n",
    "\n",
    "\\begin{equation}\n",
    "Y^\\ell(\\bf{u}_\\beta),  \\, \\forall \\, \\beta \\, \\in \\, AOI; \\, \\ell = 1, \\ldots, L\n",
    "\\end{equation}\n",
    "\n",
    "5. add the trend back in to the stochastic residual realizations to calculate the multiple realizations, $L$, of the property of interest based on the composite model of known deterministic trend, $m(\\bf{u}_\\alpha)$ and unknown stochastic residual, $y(\\bf{u}_\\alpha)$ \n",
    "\n",
    "\\begin{equation}\n",
    "Z^\\ell(\\bf{u}_\\beta) = Y^\\ell(\\bf{u}_\\beta) + m(\\bf{u}_\\beta),  \\, \\forall \\, \\beta \\in \\, AOI; \\, \\ell = 1, \\ldots, L\n",
    "\\end{equation}\n",
    "\n",
    "6. check the model, including quantification of the proportion of variance treated as known (trend) and unknown (residual).\n",
    "\n",
    "\\begin{equation}\n",
    "\\sigma^2_{Z} = \\sigma^2_{Y} + \\sigma^2_{m} + 2 \\cdot C_{Y,m}\n",
    "\\end{equation}\n",
    "\n",
    "given $C_{Y,m} \\to 0$:\n",
    "\n",
    "\\begin{equation}\n",
    "\\sigma^2_{Z} = \\sigma^2_{Y} + \\sigma^2_{m}\n",
    "\\end{equation}\n",
    "\n",
    "I can now describe the proportion of variance allocated to known and unknown components as follows:\n",
    "\n",
    "\\begin{equation}\n",
    "Prop_{Known} = \\frac{\\sigma^2_{m}}{\\sigma^2_{Y} + \\sigma^2_{m}}, \\quad Prop_{Unknown} = \\frac{\\sigma^2_{Y}}{\\sigma^2_{Y} + \\sigma^2_{m}}\n",
    "\\end{equation}\n",
    "\n",
    "I provide some practical, data-driven methods for trend model, but I should indicate that:\n",
    "\n",
    "1. trend modeling is very important in reservoir modeling as it has large impact on local model accuracy and on the undertainty model\n",
    "2. trend modeling is used in almost every subsurface model, unless the data is dense enough to impose local trends\n",
    "3. trend modeling includes a high degree of expert judgement combined with the integration of various information sources\n",
    "\n",
    "#### Danger of Overfit\n",
    "\n",
    "It follows that a sufficiently flexible, complicated trend model can perfectly fit the data. For example, a trend based on spatial interpolation would exactly honor the data at the data locations.\n",
    "\n",
    "What would be the issue with that?\n",
    "\n",
    "\\begin{equation}\n",
    "\\sigma^2_{Y} \\to 0\n",
    "\\end{equation}\n",
    "\n",
    "\\begin{equation}\n",
    "Prop_{Known} = \\frac{\\sigma^2_{m}}{\\sigma^2_{m}} = 100\\%, \\quad\n",
    "Prop_{Unknown} = \\frac{0}{\\sigma^2_{m}} = 0\\%\n",
    "\\end{equation} \n",
    "\n",
    "The result is a model that assumes that all variance is described by the deterministic trend; therefore is variance is partitioned to the known bin! This is a model without uncertainty. \n",
    "\n",
    "There's another aspect, the model that is very flexible will have more parameters to fit with the same number of data; therefore, the uncertainty in the fit of the model will increase. The model variance increases, in other words the model is more sensitive to the actually data available and is likely to be quite inaccurate away from the data.  \n",
    "\n",
    "We can observe the impact of overfit with a very simple 1D problem.\n",
    "\n",
    "#### Objective \n",
    "\n",
    "In the PGE 383: Stochastic Subsurface Modeling class I want to provide hands-on experience with building subsurface modeling workflows. Python provides an excellent vehicle to accomplish this. I have coded a package called GeostatsPy with GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functionality that provides basic building blocks for building subsurface modeling workflows. \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data file to your working directory.  They are available here:\n",
    "\n",
    "* Tabular data - sample_data_biased.csv at https://git.io/fh0CW\n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. "
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geostatspy.GSLIB as GSLIB          # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats    # GSLIB methods convert to Python        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                        # ndarrys for gridded data\n",
    "import pandas as pd                       # DataFrames for tabular data\n",
    "import os                                 # set working directory, run executables\n",
    "import matplotlib.pyplot as plt           # for plotting\n",
    "from scipy import stats                   # summary statistics\n",
    "import seaborn as sns                     # model confidence intervals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"c:/PGE383\")             # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Simple Dataset\n",
    "\n",
    "Let's make a simple dataset, permeability in 1D over the x coordinate, input in ndarrays and then combined into a Pandas DataFrame. We will also make sure that what we made are in fact ndarrays and a DataFrame.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z and perm are <class 'numpy.ndarray'>,<class 'numpy.ndarray'>\n",
      "df is type <class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>z</th>\n",
       "      <th>perm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90</td>\n",
       "      <td>320</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>1200</td>\n",
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       "      <th>3</th>\n",
       "      <td>130</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>190</td>\n",
       "      <td>340</td>\n",
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      "text/plain": [
       "     z  perm\n",
       "0   10   600\n",
       "1   90   320\n",
       "2  100  1200\n",
       "3  130   750\n",
       "4  190   340"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = np.array([10,90,100,130,190,210,250,290])\n",
    "perm = np.array([600,320,1200,750,340,1120,1690,1530])\n",
    "print('z and perm are ' +str(type(z)) + ',' + str(type(perm)))\n",
    "df = pd.DataFrame({'z':z,'perm':perm})\n",
    "print('df is type ' + str(type(df)))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, we have a dataset ready to go, let's plot the data and see what we are dealing with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(111)\n",
    "plt.scatter(df['z'].values,df['perm'].values, s=30, alpha = 0.6, edgecolors = \"black\", facecolors = 'red')\n",
    "plt.xlabel('Z (m)')\n",
    "plt.ylabel('Permeability (mD)')\n",
    "plt.title('Permeability Over X')\n",
    "plt.ylim(0.0,2000)\n",
    "plt.xlim(0,300)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.2, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems pretty clear that permeability mean is not stationary over $z$, let's fit a variety of polygonal trends including, 1st, 3rd, 5th and 7th order polynomials (in order of increasing complexity).\n",
    "\n",
    "We will plot the polynomial model fit with the (model training) data and the residual (training data - model) at sampled $z$ locations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 300, 100)\n",
    "\n",
    "p1 = np.polyfit(df['z'],df['perm'], deg=1, rcond=None, w=None, cov=False)\n",
    "m1 = np.polyval(p1,x)\n",
    "\n",
    "p3 = np.polyfit(df['z'],df['perm'], deg=3, rcond=None, w=None, cov=False)\n",
    "m3 = np.polyval(p3,x)\n",
    "\n",
    "p5 = np.polyfit(df['z'],df['perm'], deg=5, rcond=None, w=None, cov=False)\n",
    "m5 = np.polyval(p5,x)\n",
    "\n",
    "p7 = np.polyfit(df['z'],df['perm'], deg=7, rcond=None, w=None, cov=False)\n",
    "m7 = np.polyval(p7,x)\n",
    "\n",
    "plt.subplot(421)\n",
    "plt.plot(df['z'],df['perm'], 'x', c='black')\n",
    "plt.plot(x, m1, c='red')\n",
    "plt.xlim([0,300]); plt.ylim([0,2000])\n",
    "plt.xlabel('z (m)'); plt.ylabel('Permeability (mD)'); plt.title('1st Order Polynomial Trend Fit')\n",
    "\n",
    "plt.subplot(422)\n",
    "r1 = df['perm'].values - np.polyval(p1,df['z'].values)\n",
    "plt.hist(r1, facecolor='red',bins=np.linspace(-1000,1000,100),alpha=0.2,density=True,edgecolor='black')\n",
    "plt.xlim([-1000,1000]); plt.ylim([0,.01])\n",
    "plt.xlabel('Trend Residual (mD)'); plt.ylabel('Probability'); plt.title('1st Order Polynomial Trend Fit Residual')\n",
    "\n",
    "plt.subplot(423)\n",
    "plt.plot(df['z'],df['perm'], 'x', c='black')\n",
    "plt.plot(x, m3, c='red')\n",
    "plt.xlim([0,300]); plt.ylim([0,2000])\n",
    "plt.xlabel('z (m)'); plt.ylabel('Permeability (mD)'); plt.title('3rd Order Polynomial Trend Fit')\n",
    "\n",
    "plt.subplot(424)\n",
    "r3 = df['perm'].values - np.polyval(p3,df['z'].values)\n",
    "plt.hist(r3, facecolor='red',bins=np.linspace(-1000,1000,100),alpha=0.2,density=True,edgecolor='black')\n",
    "plt.xlim([-1000,1000]); plt.ylim([0,.01])\n",
    "plt.xlabel('Trend Residual (mD)'); plt.ylabel('Probability'); plt.title('3rd Order Polynomial Trend Fit Residual')\n",
    "\n",
    "plt.subplot(425)\n",
    "plt.plot(df['z'],df['perm'], 'x', c='black')\n",
    "plt.plot(x, m5, c='red')\n",
    "plt.xlim([0,300]); plt.ylim([0,2000])\n",
    "plt.xlabel('z (m)'); plt.ylabel('Permeability (mD)'); plt.title('5th Order Polynomial Trend Fit')\n",
    "\n",
    "plt.subplot(426)\n",
    "r5 = df['perm'].values - np.polyval(p5,df['z'].values)\n",
    "plt.hist(r5, facecolor='red',bins=np.linspace(-1000,1000,100),alpha=0.2,density=True,edgecolor='black')\n",
    "plt.xlim([-1000,1000]); plt.ylim([0,.01])\n",
    "plt.xlabel('Trend Residual (mD)'); plt.ylabel('Probability'); plt.title('5th Order Polynomial Trend Fit Residual')\n",
    "\n",
    "plt.subplot(427)\n",
    "plt.plot(df['z'],df['perm'], 'x', c='black')\n",
    "plt.plot(x, m7, c='red')\n",
    "plt.xlim([0,300]); plt.ylim([0,2000])\n",
    "plt.xlabel('z (m)'); plt.ylabel('Permeability (mD)'); plt.title('7th Order Polynomial Trend Fit')\n",
    "\n",
    "plt.subplot(428)\n",
    "r7 = df['perm'].values - np.polyval(p7,df['z'].values)\n",
    "plt.hist(r7, facecolor='red',bins=np.linspace(-1000,1000,100),alpha=0.2,density=True,edgecolor='black')\n",
    "plt.xlim([-1000,1000]); plt.ylim([0,.01])\n",
    "plt.xlabel('Trend Residual (mD)'); plt.ylabel('Probability'); plt.title('7th Order Polynomial Trend Fit Residual')\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=3.2, wspace=0.2, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the complexity of the model, the order of the polynomial, increases the flexibility to fit the data increases. With a 7th order polynomial we infact perfectly fit our 8 data and the residual is contant $=$ 0.  \n",
    "\n",
    "**model bias** is due to the model's inability to fit the data. e.g. if the model is too simple vs. the complexity observed in the data then the model bias is high.  With out 7th order model, model bias is at a minimum as we are able to perfectly fit the data.\n",
    "\n",
    "Going back to spatial modeling concepts, such as trend modeling, we can recognize that with our 7th order polynomial trend, we have a model that has described all variance in the data, all variance is assigned as known. This is very dangerous, indeed.  \n",
    "\n",
    "What about the ability of this model to make predictions away from the observations used to build it (training data). I withheld some testing data. Let's put them into arrays and then a DataFrame like we did before and then plot them with the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ztest = np.array([50,170,230,270])\n",
    "permtest = np.array([660,830,1040,1910])\n",
    "dftest = pd.DataFrame({'z':ztest,'perm':permtest})\n",
    "\n",
    "plt.subplot(111)\n",
    "plt.scatter(df['z'].values,df['perm'].values, s=30, alpha = 0.6, edgecolors = \"black\", facecolors = 'red')\n",
    "plt.scatter(dftest['z'].values,dftest['perm'].values, s=30, alpha = 0.6, edgecolors = \"black\", facecolors = 'blue')\n",
    "plt.xlabel('Z (m)')\n",
    "plt.ylabel('Permeability (mD)')\n",
    "plt.title('Permeability Train (red) and Test (blue) Over X')\n",
    "plt.ylim(0.0,2000)\n",
    "plt.xlim(0,300)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.2, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The testing data looks fair. They are not unusual compared to the training data, that is they do not have different ranges of values, nor represent significant discontinuities or jumps in the data, also they are not spatially extrapolated. Now let's repeat the plots for our models over a range of complexities, but this time include the testing data for comparison and plot the residual distribution for the testing data (this is testing error)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(421)\n",
    "plt.plot(df['z'],df['perm'], 'x', c='black')\n",
    "plt.plot(dftest['z'],dftest['perm'], 'o', c='black')\n",
    "plt.plot(x, m1, c='red')\n",
    "plt.xlim([0,300]); plt.ylim([0,2000])\n",
    "plt.xlabel('z (m)'); plt.ylabel('Permeability (mD)'); plt.title('1st Order Polynomial Trend Fit')\n",
    "\n",
    "plt.subplot(422)\n",
    "r1test = dftest['perm'].values - np.polyval(p1,dftest['z'].values)\n",
    "plt.hist(r1test, facecolor='red',bins=np.linspace(-15000,15000,100),alpha=0.2,density=True,edgecolor='black')\n",
    "plt.xlim([-15000,15000]); plt.ylim([0,.003])\n",
    "plt.xlabel('Test Error (mD)'); plt.ylabel('Probability'); plt.title('1st Order Polynomial Trend Fit Test Error')\n",
    "\n",
    "plt.subplot(423)\n",
    "plt.plot(df['z'],df['perm'], 'x', c='black')\n",
    "plt.plot(dftest['z'],dftest['perm'], 'o', c='black')\n",
    "plt.plot(x, m3, c='red')\n",
    "plt.xlim([0,300]); plt.ylim([0,2000])\n",
    "plt.xlabel('z (m)'); plt.ylabel('Permeability (mD)'); plt.title('3rd Order Polynomial Trend Fit')\n",
    "\n",
    "plt.subplot(424)\n",
    "r3test = dftest['perm'].values - np.polyval(p3,dftest['z'].values)\n",
    "plt.hist(r3test, facecolor='red',bins=np.linspace(-15000,15000,100),alpha=0.2,density=True,edgecolor='black')\n",
    "plt.xlim([-15000,15000]); plt.ylim([0,.003])\n",
    "plt.xlabel('Trend Residual (mD)'); plt.ylabel('Probability'); plt.title('3rd Order Polynomial Trend Fit Test Error')\n",
    "\n",
    "plt.subplot(425)\n",
    "plt.plot(df['z'],df['perm'], 'x', c='black')\n",
    "plt.plot(dftest['z'],dftest['perm'], 'o', c='black')\n",
    "plt.plot(x, m5, c='red')\n",
    "plt.xlim([0,300]); plt.ylim([0,2000])\n",
    "plt.xlabel('z (m)'); plt.ylabel('Permeability (mD)'); plt.title('5th Order Polynomial Trend Fit')\n",
    "\n",
    "plt.subplot(426)\n",
    "r5test = dftest['perm'].values - np.polyval(p5,dftest['z'].values)\n",
    "plt.hist(r5test, facecolor='red',bins=np.linspace(-15000,15000,100),alpha=0.2,density=True,edgecolor='black')\n",
    "plt.xlim([-15000,15000]); plt.ylim([0,.003])\n",
    "plt.xlabel('Trend Residual (mD)'); plt.ylabel('Probability'); plt.title('5th Order Polynomial Trend Fit Test Error')\n",
    "\n",
    "plt.subplot(427)\n",
    "plt.plot(df['z'],df['perm'], 'x', c='black')\n",
    "plt.plot(dftest['z'],dftest['perm'], 'o', c='black')\n",
    "plt.plot(x, m7, c='red')\n",
    "plt.xlim([0,300]); plt.ylim([0,2000])\n",
    "plt.xlabel('z (m)'); plt.ylabel('Permeability (mD)'); plt.title('7th Order Polynomial Trend Fit')\n",
    "\n",
    "plt.subplot(428)\n",
    "r7test = dftest['perm'].values - np.polyval(p7,dftest['z'].values)\n",
    "plt.hist(r7test, facecolor='red',bins=np.linspace(-15000,15000,100),alpha=0.2,density=True,edgecolor='black')\n",
    "plt.xlim([-15000,15000]); plt.ylim([0,.003])\n",
    "plt.xlabel('Trend Residual (mD)'); plt.ylabel('Probability'); plt.title('7th Order Polynomial Trend Test Error')\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=3.2, wspace=0.2, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See what happenned? As the complexity increased, our model becomes more inaccurate for the testing data. \n",
    "\n",
    "**model variance** is the sensitivity of the model to the exact data provided for training. Our 1st order model was not very sensitive to the training data, swapping training data with testing data would not have changed the model very much. Yet, with the 7th order model, making this swap would have dramatically changed the model.\n",
    "\n",
    "Getting back to spatial modeling, with our complicated trend model, we have fit all the idiosyncracies in the data resulting in a very poor prediction model away from the data. This complicated model would not be a defendable trend model!\n",
    "\n",
    "Can statistics help us further communicate that the complicated model is **not** a good model? You bet, we can plot the confidence interval in the trend models. This is an envelope of 95% uncertainty in the entire trend model given a limited number of noisy data (not to be confused with prediction interval for the next observation).    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "f,axes = plt.subplots(2,2,figsize=(10, 6))\n",
    "\n",
    "lm1 = sns.regplot(df['z'],df['perm'], order=1,color ='blue',ax=axes[0, 0])\n",
    "axes1 = lm1.axes; axes1.set_xlim(0,300); axes1.set_ylim(-2000,4000)\n",
    "lm1.set(xlabel='X (m)', ylabel='Permeability (mD)', title='1st Order Polynomial Fit and Confidence Interval')\n",
    "\n",
    "lm3 = sns.regplot(df['z'],df['perm'], order=3,color ='blue',ax=axes[0, 1])\n",
    "axes3 = lm3.axes; axes3.set_xlim(0,300); axes3.set_ylim(-2000,4000)\n",
    "lm3.set(xlabel='X (m)', ylabel='Permeability (mD)', title='3rd Order Polynomial Fit and Confidence Interval')\n",
    "\n",
    "lm5 = sns.regplot(df['z'],df['perm'], order=5,color ='blue',ax=axes[1, 0])\n",
    "axes5 = lm5.axes; axes5.set_xlim(0,300); axes5.set_ylim(-2000,4000)\n",
    "lm5.set(xlabel='X (m)', ylabel='Permeability (mD)', title='5th Order Polynomial Fit and Confidence Interval')\n",
    "\n",
    "lm7 = sns.regplot(df['z'],df['perm'], order=7,color ='blue',ax=axes[1, 1])\n",
    "axes7 = lm7.axes; axes7.set_xlim(0,300); axes7.set_ylim(-2000,4000)\n",
    "lm7.set(xlabel='X (m)', ylabel='Permeability (mD)', title='7th Order Polynomial Fit and Confidence Interval')\n",
    "\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due to the few data available, the confidence intervals expand repidly as the complexity increases. Statistics is telling us that we have no confidence in our model!  We do not have enough data to justify a complicated trend model.  \n",
    "\n",
    "A quick caveat - if there is ancillary information to support a more complicated trend model then we should integrate it. For example, geological, engineering and geophyscial concepts may provide further support. Spatial trend modeling is not just about fitting the data!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "This was a basic demonstration of overfitting in trend modeling. Much more could be done, such as randomizing the data and training observe model variance directly and use of other types of trend models (e.g. moving window averaging / convoluational models). \n",
    "\n",
    "I have other demonstrations on the basics of working with DataFrames, ndarrays and many other workflows availble at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy.\n",
    "\n",
    "I hope this was helpful,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
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